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A Baby Steps/Giant Steps Probabilistic Algorithm for Computing Roadmaps in Smooth Bounded Real Hypersurface

机译:光滑有界实超曲面中计算路线图的婴儿步长/巨型步长概率算法

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摘要

We consider the problem of constructing roadmaps of real algebraic sets. This problem was introduced by Canny to answer connectivity questions and solve motion planning problems. Given s polynomial equations with rational coefficients, of degree D in n variables, Canny's algorithm has a Monte Carlo cost of sn log(s)D~o(n~2 operations in ?; a deterministic version runs in time sn log(s)D~o(n~2. A subsequent improvement was due to Basu, Pollack, and Roy, with an algorithm of deterministic cost s~(d+1)D~0(n~2 for the more general problem of computing roadmaps of a semi-algebraic set (d≤n is the dimension of an associated object). We give a probabilistic algorithm of complexity (nD)~0(n~(1.5) for the problem of computing a roadmap of a closed and bounded hypersurface V of degree D in n variables, with a finite number of singular points. Even under these extra assumptions, no previous algorithm featured a cost better than D~0(n~2.
机译:我们考虑构建实数代数集路线图的问题。 Canny引入了此问题,以回答连通性问题并解决运动计划问题。给定s个具有n个变量中度D的有理系数的多项式方程,Canny的算法具有sn logD〜o(?中的n〜2个运算)的蒙特卡洛成本;确定性版本在时间sn log中运行D〜o(n〜2。随后的改进归因于Basu,Pollack和Roy,它采用了确定性成本s〜(d + 1)D〜0(n〜2的算法),用于计算路线图的更一般的问题。一个半代数集(d≤n是关联对象的维数)。我们针对计算封闭有界超曲面V的路线图的问题给出了概率复杂度(nD)〜0(n〜(1.5)的概率算法在n个变量中具有有限数量的奇异点的D阶,即使在这些额外的假设下,以前的算法也没有比D〜0(n〜2)更好的代价。

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